MATEC Web Conf.
Volume 139, 20172017 3rd International Conference on Mechanical, Electronic and Information Technology Engineering (ICMITE 2017)
|Number of page(s)||4|
|Published online||05 December 2017|
CNN-Based Vision Model for Obstacle Avoidance of Mobile Robot
1 School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China
2 Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China
Exploration in a known or unknown environment for a mobile robot is an essential application. In the paper, we study the mobile robot obstacle avoidance problem in an indoor environment. We present an end-to-end learning model based Convolutional Neural Network (CNN), which takes the raw image obtained from camera as only input. And the method converts directly the raw pixels to steering commands including turn left, turn right and go straight. Training data was collected by a human remotely controlled mobile robot which was manipulated to explore in a structure environment without colliding into obstacles. Our neural network was trained under caffe framework and specific instructions are executed by the Robot Operating System (ROS). We analysis the effect of the datasets from different environments with some marks on training process and several real-time detect experiments were designed. The final test result shows that the accuracy can be improved by increase the marks in a structured environment and our model can get high accuracy on obstacle avoidance for mobile robots.
Key words: End-to-End Learning / CNN; / Obstacle Avoidance / ROS
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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